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GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease

The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data...

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Autores principales: Zhang, Yutao, Xi, Zhengtao, Zheng, Jiahui, Shi, Haifeng, Jiao, Zhuqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850953/
https://www.ncbi.nlm.nih.gov/pubmed/35185530
http://dx.doi.org/10.3389/fnagi.2022.834331
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author Zhang, Yutao
Xi, Zhengtao
Zheng, Jiahui
Shi, Haifeng
Jiao, Zhuqing
author_facet Zhang, Yutao
Xi, Zhengtao
Zheng, Jiahui
Shi, Haifeng
Jiao, Zhuqing
author_sort Zhang, Yutao
collection PubMed
description The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD.
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spelling pubmed-88509532022-02-18 GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease Zhang, Yutao Xi, Zhengtao Zheng, Jiahui Shi, Haifeng Jiao, Zhuqing Front Aging Neurosci Neuroscience The scores of the cognitive function of patients with end-stage renal disease (ESRD) are highly subjective, which tend to affect the results of clinical diagnosis. To overcome this issue, we proposed a novel model to explore the relationship between functional magnetic resonance imaging (fMRI) data and clinical scores, thereby predicting cognitive function scores of patients with ESRD. The model incorporated three parts, namely, graph theoretic algorithm (GTA), whale optimization algorithm (WOA), and least squares support vector regression machine (LSSVRM). It was called GTA-WOA-LSSVRM or GWLS for short. GTA was adopted to calculate the area under the curve (AUC) of topological parameters, which were extracted as the features from the functional networks of the brain. Then, the statistical method and Pearson correlation analysis were used to select the features. Finally, the LSSVRM was built according to the selected features to predict the cognitive function scores of patients with ESRD. Besides, WOA was introduced to optimize the parameters in the LSSVRM kernel function to improve the prediction accuracy. The results validated that the prediction accuracy obtained by GTA-WOA-LSSVRM was higher than several comparable models, such as GTA-SVRM, GTA-LSSVRM, and GTA-WOA-SVRM. In particular, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) between the predicted scores and the actual scores of patients with ESRD were 0.92, 0.88, and 4.14%, respectively. The proposed method can more accurately predict the cognitive function scores of ESRD patients and thus helps to understand the pathophysiological mechanism of cognitive dysfunction associated with ESRD. Frontiers Media S.A. 2022-02-03 /pmc/articles/PMC8850953/ /pubmed/35185530 http://dx.doi.org/10.3389/fnagi.2022.834331 Text en Copyright © 2022 Zhang, Xi, Zheng, Shi and Jiao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Zhang, Yutao
Xi, Zhengtao
Zheng, Jiahui
Shi, Haifeng
Jiao, Zhuqing
GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
title GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
title_full GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
title_fullStr GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
title_full_unstemmed GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
title_short GWLS: A Novel Model for Predicting Cognitive Function Scores in Patients With End-Stage Renal Disease
title_sort gwls: a novel model for predicting cognitive function scores in patients with end-stage renal disease
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850953/
https://www.ncbi.nlm.nih.gov/pubmed/35185530
http://dx.doi.org/10.3389/fnagi.2022.834331
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